Discovery of search objectives in continuous domains
Paweł Liskowski, Krzysztof Krawiec · Proceedings of the Genetic and Evolutionary Computation Conference · 2017
In genetic programming (GP), the outcomes of the evaluation phase can be represented as an interaction matrix, with rows corresponding to programs in a population and columns corresponding to tests that define a program synthesis task. Recent contributions on Discovery of Objectives via Clustering (DOC) and Discovery of Objectives by Factorization of interaction matrix (DOF) show that informative characterizations of programs can be automatically derived from interaction matrices in discrete domains and used as search objectives in multidimensional setting. In this paper, we propose analogous methods for continuous domains and compare them with conventional GP that uses tournament selection, Age-Fitness Pareto Optimization, and GP with epsilon-lexicase selection. Experiments show that the proposed methods are effective for symbolic regression, systematically producing better-fitting models than the two former baselines, and surpassing epsilon-lexicase selection on some problems. We also investigate the hybrids of the proposed approach with the baselines, concluding that hybridization of DOC with epsilon-lexicase leads to the best overall results.